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Complete fracture healing is crucial for positive patient outcome. A major complication in fracture treatment is non-union. Infection is among the main causes of non-union and hence of osteosynthesis failure. For the treatment of non-union, it is crucial to understand whether a fracture is not healing because of an underlying septic process, since the surgical approach to non-unions definitely differs according to whether the fracture is infected or aseptic. We aimed to assess the diagnostic performance of 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography-computed tomography ([18F]FDG PET/CT) in the evaluation of infection as possible cause of non-union. We retrospectively evaluated images of 47 patients treated in our trauma center who, between January 2011 and June 2017, underwent preoperative [18F]FDG PET/CT aiming to exclude infection in non-union. Clinical data, diagnostic examinations, laboratory and microbiology results, and patient outcome were collected and analyzed. [18F]FDG PET/CT images were visually and semiquantitatively evaluated using the maximum standardized uptake value (SUVmax). Imaging findings, as assessed by an experienced nuclear medicine physician and an experienced musculoskeletal radiologist, were compared with intraoperative microbiological culture results, which were used for final diagnosis (reference standard). The diagnostic performance of [18F]FDG PET/CT in detecting infected non-union was assessed. Twenty-two patients were not infected, while the remaining 25 had positive intraoperative microbiological results. C-reactive protein (CRP) was within the normal range in 13 cases (five with a final diagnosis of infection) and higher than normal in 25 patients (13 with a final diagnosis of infection). Infection was correctly detected on visual analysis of PET/CT images in 23 cases, while 2/25 infected patients had no significant [18F]FDG uptake and were considered false negatives. In seven cases, [18F]FDG PET/CT showed false positive results; 15/22 disease-free patients were correctly diagnosed. The diagnostic accuracy of [18F]FDG PET/CT in the final diagnosis of infection was 81% (38/47); its sensitivity, specificity, positive predictive value, and negative predictive value were 92%, 68%, 77%, and 88% respectively. The likelihood ratio for a positive test (LR+) was 2.89 and for a negative test, 0.12. Pretest probability of disease was 53%. Post-test probability based on LR+ was 77%. [18F]FDG PET/CT is a promising tool for diagnoses of infected non-unions. Both PET and CT images should be interpreted to achieve a high sensitivity (92%) and a very good negative post-test probability (12%).

To assess the role of radiomics parameters in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer. Seventy-nine patients who had undergone pretreatment staging 18F-FDG PET/CT and treatment with NAC between January 2010 and January 2018 were included in the study. Primary lesions on PET images were delineated, and extraction of first-, second-, and higher-order imaging features was performed using LIFEx software. The relationship between these parameters and pCR to NAC was analyzed by multiple logistic regression models. Nineteen patients (24%) had pCR to NAC. Different models were generated on complete information and imputed datasets, using univariable and multivariable logistic regression and least absolute shrinkage and selection operator (lasso) regression. All models could predict pCR to NAC, with area under the curve values ranging from 0.70 to 0.73. All models agreed that tumor molecular subtype is the primary predictor of the primary endpoint. Our models predicted that patients with subtype 2 and subtype 3 (HER2+ and triple negative, respectively) are more likely to have a pCR to NAC than those with subtype 1 (luminal). The association between PET imaging features and pCR suggested that PET imaging features could be considered as potential predictors of pCR in locally advanced breast cancer patients.

To evaluate the ability of CT and PET radiomics features to classify lung lesions as primary or metastatic, and secondly to differentiate histological subtypes of primary lung cancers. A cohort of 534 patients with lung lesions were retrospectively studied. Radiomics texture features were extracted using the LIFEx package from semiautomatically segmented PET and CT images. Histology data were recorded in all patients. The patient cohort was divided into a training and a validation group and linear discriminant analysis (LDA) was performed to classify the lesions using both direct and backward stepwise methods. The robustness of the procedure was tested by repeating the entire process 100 times with different assignments to the training and validation groups. Scoring metrics included analysis of the receiver operating characteristic curves in terms of area under the curve (AUC), sensitivity, specificity and accuracy. Radiomics features extracted from CT and PET datasets were able to differentiate primary tumours from metastases in both the training and the validation group (AUCs 0.79 ± 0.03 and 0.70 ± 0.04, respectively, from the CT dataset; AUCs 0.92 ± 0.01 and 0.91 ± 0.03, respectively, from the PET dataset). The AUC cut-off thresholds identified by LDA using direct and backward elimination strategies were −0.79 ± 0.06 and −0.81 ± 0.08, respectively (CT dataset) and −0.69 ± 0.05 and −0.68 ± 0.04, respectively (PET dataset). For differentiation between primary subgroups based on CT features, the AUCs in the training and validation groups were 0.81 ± 0.02 and 0.69 ± 0.04 for adenocarcinoma (Adc) vs. squamous cell carcinoma (Sqc) or “Other”, 0.85 ± 0.02 and 0.70 ± 0.05 for Sqc vs. Adc or Other, and 0.77 ± 0.03 and 0.57 ± 0.05 for Other vs. Adc or Sqc. The same analyses for the PET data revealed AUCs of 0.90 ± 0.10 and 0.80 ± 0.04, 0.80 ± 0.02 and 0.61 ± 0.06, and 0.97 ± 0.01 and 0.88 ± 0.04, respectively. PET radiomics features were able to differentiate between primary and metastatic lung lesions and showed the potential to identify primary lung cancer subtypes.

Aim: During our daily clinical practice using 11C-Choline PET/CT for restaging patients affected by relapsing prostate cancer (rPCa) we noticed an unusual but significant occurrence of hypodense hepatic lesions with a different tracer uptake. Thus, we decided to evaluate the possible correlation between rPCa and these lesions as possible hepatic metastases. Materials and methods: We retrospectively enrolled 542 patients diagnosed with rPCa in biochemical relapse after a radical treatment (surgery and/or radiotherapy). Among these, patients with a second tumor or other benign hepatic diseases were excluded. All patients underwent 11C-Choline PET/CT during the standard restaging workup of their disease. We analyzed CT images to evaluate the presence of hypodense lesions and PET images to identify the relative tracer uptake. In accordance to the subsequent oncological history, five clinical scenarios were recognized [Table 1]: normal low dose CT (ldCT) and normal tracer distribution (Group A); evidence of previously unknown hepatic round hypodense areas at ldCT with normal rim uptake (Group B); evidence of previously known hepatic round hypodense areas at ldCT stable over time and with normal rim uptake (Group C); evidence of previously known hepatic round hypodense areas at ldCT, in a previous PET/CT scan, with or without rim uptake and significantly changing over time in terms of size and/or uptake (Group D); evidence of hepatic round hypodense areas at ldCT with or without rim uptake confirmed as prostate liver metastases by histopathology, triple phase ceCT, ce-ultra sound (CEUS) and clinical/biochemical evaluation (Group E). We evaluated the correlation with PSA level at time of scan, rim SUVmax and association with local relapse or non-hepatic metastases (lymph nodes, bone, other parenchyma). Results: Five hundred and forty-two consecutive patients were retrospectively enrolled. In 140 of the 542 patients more than one 11C-choline PET/CT had been performed. A total of 742 11C-Choline PET/CT scans were analyzed. Of the 542 patients enrolled, 456 (84.1%) had a normal appearance of the liver both at ldCT and PET (Group A). 19/542 (3,5%) belonged to Group B, 13/542 (2.4%) to Group C, 37/542 (6.8%) to Group D and 18/542 (3.3%) to Group E. Mean SUVmax of the rim was: 4.5 for Group B; 4.2 for Group C; 4.8 for Group D; 5.9 for Group E. Mean PSA level was 5.27 for Group A, 7.9 for Group B, 10.04 for Group C, 10.01 for Group D, 9.36 for Group E. Presence of positive findings at 11C-Choline PET/CT in any further anatomical area (local relapse, lymph node, bone, other extra hepatic sites) correlated with an higher PSA (p = 0.0285). In both the univariate and multivariate binary logistic regression analyses. PSA, SUVmax of the rim, local relapse, positive nodes were not associated to liver mets (Groups D-E) (p > 0.05). On the contrary, a significant correlation was found between the presence of liver metG (group D-E) and bone lesions (p= 0.00193). Conclusion: Our results indicate that liver metastases in relapsing prostate cancer may occur frequently. The real incidence evaluation needs more investigations. In this case and despite technical limitations, Choline PET/CT shows alterations of tracer distribution within the liver that could eventually be mistaken for simple cysts but can be suspected when associated to high trigger PSA, concomitant bone lesions or modification over time. In this clinical setting an accurate analysis of liver tracer distribution (increased or decreased uptake) by the nuclear medicine physician is, therefore, mandatory.

Radiomic features derived from the texture analysis of different imaging modalities e show promise in lesion characterisation, response prediction, and prognostication in lung cancer patients. The present study aimed to identify an images-based radiomic signature capable of predicting disease-free survival (DFS) in non-small cell lung cancer (NSCLC) patients undergoing surgery. A cohort of 295 patients was selected. Clinical parameters (age, sex, histological type, tumour grade, and stage) were recorded for all patients. The endpoint of this study was DFS. Both computed tomography (CT) and fluorodeoxyglucose positron emission tomography (PET) images generated from the PET/CT scanner were analysed. Textural features were calculated using the LifeX package. Statistical analysis was performed using the R platform. The datasets were separated into two cohorts by random selection to perform training and validation of the statistical models. Predictors were fed into a multivariate Cox proportional hazard regression model and the receiver operating characteristic (ROC) curve as well as the corresponding area under the curve (AUC) were computed for each model built. The Cox models that included radiomic features for the CT, the PET, and the PET+CT images resulted in an AUC of 0.75 (95%CI: 0.65–0.85), 0.68 (95%CI: 0.57–0.80), and 0.68 (95%CI: 0.58–0.74), respectively. The addition of clinical predictors to the Cox models resulted in an AUC of 0.61 (95%CI: 0.51–0.69), 0.64 (95%CI: 0.53–0.75), and 0.65 (95%CI: 0.50–0.72) for the CT, the PET, and the PET+CT images, respectively. A radiomic signature, for either CT, PET, or PET/CT images, has been identified and validated for the prediction of disease-free survival in patients with non-small cell lung cancer treated by surgery.

The technological advances in imaging and production of radiopharmaceuticals are driving an innovative way of evaluating the targets for antineoplastic therapies. Besides the use of imaging to better delineate the volume of external beam radiation therapy in oncology, modern imaging techniques are able to identify targets for highly specific medical therapies, using chemotherapeutic drugs and antiangiogenesis molecules. Moreover, radionuclide imaging is able to select targets for radionuclide therapy and to give the way to in vivo dose calculation to target tissues and to critical organs. This contribution reports the main studies published on matched pairs dosimetry with (124)I/(131)I- and (86)Y/(90)Y-labelled radiopharmaceuticals, with an emphasis on metaiodobenzylguanidine (MIBG) and monoclonal antibodies.